Prepare Dataset for AccentOptimizer

[AhmedFatthy1040]

Prepare Dataset for AccentOptimizer

Description:
We need to prepare a suitable dataset for training the pronunciation evaluation machine learning model. This dataset should include audio samples and corresponding labels (e.g., phonemes, words, sentences) to assess pronunciation accuracy. Preparing the dataset will involve sourcing, cleaning, and organizing the data for efficient model training.

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Tasks:

1. Source a Dataset:

   • Research and select a public dataset of English speech/audio recordings.
   • Ensure the dataset contains relevant phoneme/word pronunciation labels for evaluation.
   • Verify dataset licensing to ensure proper usage.

2. Clean and Preprocess Audio Data:

   • Standardize audio formats (e.g., WAV, MP3) and ensure consistent sample rates.
   • Remove background noise and normalize the volume for cleaner input data.
   • Segment audio files if necessary (e.g., split long recordings into smaller phonemes/words).

3. Label Data (if needed):

   • If the dataset lacks sufficient labels, manually label or use automated tools to align audio with text.

4. Organize Data for Training:

   • Structure the dataset into appropriate directories or files for training (e.g., training, validation, and test sets).
   • Ensure audio files and labels are paired correctly for seamless integration with the model.

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Expected Outcome:

• A fully prepared dataset of clean, labeled audio samples, ready for model training.
• Properly structured data that can be loaded efficiently for use in the machine learning pipeline.

[ShrouqSalem]

assign it to me